Machines (Dec 2023)

Structural Design with Self-Weight and Inertial Loading Using Simulated Annealing for Non-Gradient Topology Optimization

  • Hossein Rostami Najafabadi,
  • Thiago C. Martins,
  • Marcos S. G. Tsuzuki,
  • Ahmad Barari

DOI
https://doi.org/10.3390/machines12010025
Journal volume & issue
Vol. 12, no. 1
p. 25

Abstract

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This paper explores implementation of self-weight and inertial loading in topology optimization (TO) employing the Simulated Annealing (SA) algorithm as a non-gradient-based technique. This method can be applied to find optimum design of structures with no need for gradient information. To enhance the convergence of the SA algorithm, a novel approach incorporating the crystallization factor is introduced. The method is applied in a benchmark problem of a cantilever beam. The study systematically examines multiple scenarios, including cases with and without self-weight effects, as well as varying point loads. Compliance values are calculated and compared to those reported in existing literature to validate the accuracy of the optimization results. The findings highlight the versatility and effectiveness of the SA-based TO methodology in addressing complex design challenges with considerable self-weight or inertial effect. This work can contribute to structural design of systems where only the objective value is available with no gradient information to use sensitivity-based algorithms.

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